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Full-Text Articles in Medicine and Health Sciences
Whole-Word Recognition From Articulatory Movements For Silent Speech Interfaces, Jun Wang, Ashok Samal, Jordan R. Green, Frank Rudzicz
Whole-Word Recognition From Articulatory Movements For Silent Speech Interfaces, Jun Wang, Ashok Samal, Jordan R. Green, Frank Rudzicz
Department of Special Education and Communication Disorders: Faculty Publications
Articulation-based silent speech interfaces convert silently produced speech movements into audible words. These systems are still in their experimental stages, but have significant potential for facilitating oral communication in persons with laryngectomy or speech impairments. In this paper, we report the result of a novel, real-time algorithm that recognizes whole-words based on articulatory movements. This approach differs from prior work that has focused primarily on phoneme-level recognition based on articulatory features. On average, our algorithm missed 1.93 words in a sequence of twenty-five words with an average latency of 0.79 seconds for each word prediction using a data set of …
Sentence Recognition From Articulatory Movements For Silent Speech Interfaces, Jun Wang, Ashok Samal, Jordan R. Green, Frank Rudzicz
Sentence Recognition From Articulatory Movements For Silent Speech Interfaces, Jun Wang, Ashok Samal, Jordan R. Green, Frank Rudzicz
Department of Special Education and Communication Disorders: Faculty Publications
Recent research has demonstrated the potential of using an articulation-based silent speech interface for command-and-control systems. Such an interface converts articulation to words that can then drive a text-to-speech synthesizer. In this paper, we have proposed a novel near-time algorithm to recognize whole-sentences from continuous tongue and lip movements. Our goal is to assist persons who are aphonic or have a severe motor speech impairment to produce functional speech using their tongue and lips. Our algorithm was tested using a functional sentence data set collected from ten speakers (3012 utterances). The average accuracy was 94.89% with an average latency of …
Vowel Recognition From Continuous Articulatory Movements For Speaker-Dependent Applications, Jun Wang, Jordan R. Green, Ashok Samal, Tom D. Carrell
Vowel Recognition From Continuous Articulatory Movements For Speaker-Dependent Applications, Jun Wang, Jordan R. Green, Ashok Samal, Tom D. Carrell
Department of Special Education and Communication Disorders: Faculty Publications
A novel approach was developed to recognize vowels from continuous tongue and lip movements. Vowels were classified based on movement patterns (rather than on derived articulatory features, e.g., lip opening) using a machine learning approach. Recognition accuracy on a single-speaker dataset was 94.02% with a very short latency. Recognition accuracy was better for high vowels than for low vowels. This finding parallels previous empirical findings on tongue movements during vowels. The recognition algorithm was then used to drive an articulation-to-acoustics synthesizer. The synthesizer recognizes vowels from continuous input stream of tongue and lip movements and plays the corresponding sound samples …